Electrochemical energy storage optimal configuration method and system considering renewable energy utilization rate

Abstract

The invention belongs to the technical field of power system planning, and discloses an electrochemical energy storage optimal configuration method and system considering the renewable energy utilization rate, and the method comprises the steps: building a renewable energy output volatility index, and carrying out the site selection of electrochemical energy storage based on the index; taking the energy storage capacity merged into each selected power grid node for configuring energy storage as a variable, establishing an electrochemical energy storage optimal configuration model, and setting an objective function and constraint conditions of the model; and solving the electrochemical energy storage optimal configuration model by using a particle swarm algorithm based on inertia weight to obtain the optimal energy storage configuration capacity. According to the method, renewable energy source fluctuation rate indexes are adopted for energy storage site selection, the electrochemical energy storage optimal configuration model is established, the model is solved by adopting the particle swarm optimization algorithm considering the inertia weight, the optimal energy storage capacity is obtained, and technical support and a solution are provided for large-scale application of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to an electrochemical energy storage optimization configuration method and system taking into account renewable energy utilization. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid growth of the global economy and the continuous expansion of the population, the consumption of traditional fossil fuels such as coal, oil and natural gas has increased rapidly, and the problems of resource depletion and environmental pollution have become increasingly serious. In particular, the intensification of the greenhouse effect has had a huge impact on the global climate. The development of renewable energy to cope with the energy crisis and environmental challenges has become a global consensus.

[0004] However, with the access of large-scale renewable energy sources such as wind power and photovoltaic power to the power grid, their randomness, volatility and uncertainty have brought great challenges to the safe and stable operation of the power grid. To solve this problem, energy storage technology, especially electrochemical energy storage technology, has been widely used in power grid peak regulation and supply and demand balance due to its fast response, high efficiency, adjustability and environmental friendliness. At present, the research on the optimal configuration of electrochemical energy storage technology is still incomplete. Especially when considering the utilization rate of renewable energy, how to scientifically and reasonably configure the electrochemical energy storage system to maximize the stability of the power grid and the utilization rate of renewable energy is still an urgent problem to be solved. Therefore, conducting research on the optimal configuration of electrochemical energy storage taking into account the utilization rate of renewable energy is of great significance to promoting the sustainable development of renewable energy and improving the stability and reliability of the power grid. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes an electrochemical energy storage optimization configuration method and system taking into account the utilization rate of renewable energy, adopts the renewable energy volatility index to select the energy storage site, establishes an electrochemical energy storage optimization configuration model, and uses the particle swarm algorithm considering the inertia weight to solve the model to obtain the optimal energy storage capacity, thereby providing technical support and solutions for the large-scale application of renewable energy.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides an electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy, comprising the following steps:

[0008] Establishing renewable energy output volatility indicators and selecting electrochemical energy storage sites based on the indicators;

[0009] The energy storage capacity of each grid node selected for energy storage configuration is used as a variable to establish an electrochemical energy storage optimization configuration model, and the objective function and constraint conditions of the model are set;

[0010] The inertia weight-based particle swarm algorithm is used to solve the electrochemical energy storage optimization configuration model and obtain the optimal energy storage configuration capacity.

[0011] As an optional implementation, the renewable energy output volatility index is the ratio of the first-order difference of two output powers separated by a certain time in the wind power output time series to the power reference value.

[0012] As an optional implementation, the site selection for electrochemical energy storage is performed based on the indicators, specifically:

[0013] Calculate the output fluctuation rate of renewable energy at each node before adding energy storage to the sending-end system;

[0014] Configure energy storage of the same capacity at each renewable energy node and calculate the output fluctuation rate of renewable energy respectively;

[0015] The renewable energy output volatility of each node after adding energy storage is subtracted from the renewable energy output volatility of the corresponding node before adding energy storage, the differences are sorted, and N nodes with the most obvious reduction in renewable energy output volatility are selected to configure energy storage.

[0016] As an optional implementation method, the objective function is to minimize the overall configuration capacity of energy storage, and the constraints include basic constraints and advanced constraints. The basic constraints include system constraints and energy storage constraints.

[0017] Among them, the system constraints include power balance constraints, renewable energy power constraints and node voltage constraints, and the energy storage constraints include energy storage charging and discharging power constraints, energy storage charge state constraints and charging and discharging amount constraints.

[0018] As an optional implementation, the advanced constraint condition is a renewable energy utilization rate constraint, and its mathematical expression is:

[0019] η≥η1

[0020] Where η is the utilization rate of renewable energy, and η1 is the specified value of the utilization rate of renewable energy.

[0021] As an optional implementation, the inertia weight-based particle swarm algorithm is an improved particle swarm algorithm, which controls the inertia weight by introducing new control particles, accelerates the convergence speed, and avoids falling into the local optimum.

[0022] In a second aspect, the present invention provides an electrochemical energy storage optimization configuration system taking into account the utilization rate of renewable energy, comprising:

[0023] The energy storage site selection module is configured to: establish a renewable energy output volatility index and select a site for electrochemical energy storage based on the index;

[0024] The model building module is configured to: take the energy storage capacity incorporated into each grid node of the selected energy storage configuration as a variable, establish an electrochemical energy storage optimization configuration model, and set the objective function and constraint conditions of the model;

[0025] The model solving module is configured to solve the electrochemical energy storage optimization configuration model using a particle swarm algorithm based on inertia weight to obtain the optimal energy storage configuration capacity.

[0026] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is completed.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0028] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present disclosure proposes an electrochemical energy storage optimization configuration method and system that takes into account the utilization rate of renewable energy. When optimizing the configuration of electrochemical energy storage, in order to take into account the utilization rate of renewable energy, the renewable energy volatility index is used to select the energy storage site, and the minimum overall configuration capacity of energy storage is taken as the objective function. The electrochemical energy storage optimization configuration model is established by considering basic constraints and advanced constraints, and the model is solved by a particle swarm algorithm that considers inertia weights to obtain the optimal energy storage capacity. The electrochemical energy storage optimization configuration method disclosed in the present disclosure fully considers the energy storage charging and discharging power, state of charge constraints and charging and discharging capacity constraints under the premise of ensuring the system power balance constraints, renewable energy generation power constraints and node voltage constraints, improves the renewable energy utilization rate of the sending-end power system containing a high proportion of renewable energy, and obtains the relationship between the utilization rate of renewable energy and the configuration of energy storage capacity, which provides ideas for other scholars to study the sending-end power system containing a high proportion of renewable energy, reduces the configuration capacity of electrochemical energy storage, improves the economy of the overall configuration of the sending-end power grid, and provides technical support and solutions for the large-scale application of renewable energy. Based on the historical output data of a certain region, simulation was carried out to determine the minimum energy storage required for different renewable energy utilization rates and different charging and discharging times, and a relationship curve between renewable energy utilization rate and energy storage capacity was drawn.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A flow chart of an electrochemical energy storage optimization configuration method taking into account renewable energy utilization provided in Example 1 of the present invention;

[0034] Figure 2 A flow chart of a particle swarm algorithm based on inertia weight provided in Example 1 of the present invention;

[0035] Figure 3 It is a schematic diagram of the structure of the power system in a certain area;

[0036] Figure 4 This is a curve diagram showing the relationship between renewable energy utilization and energy storage configuration capacity. DETAILED DESCRIPTION

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0040] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0041] Example 1

[0042] like Figure 1 As shown, this embodiment provides an electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy, comprising the following steps:

[0043] S1 establishes a renewable energy output volatility index and selects a site for electrochemical energy storage based on the index.

[0044] S2 takes the energy storage capacity incorporated into each grid node of the selected energy storage configuration as a variable, establishes an electrochemical energy storage optimization configuration model, and sets the objective function and constraint conditions of the model.

[0045] S3 uses a particle swarm algorithm based on inertia weight to solve the electrochemical energy storage optimization configuration model and obtain the optimal energy storage configuration capacity.

[0046] Among them, the renewable energy output volatility index is defined as the ratio of the first-order difference of two output powers separated by a certain time in the wind power output time series to the power reference value, and its mathematical expression is:

[0047]

[0048] Where P t P is the renewable energy output at time t; t-Δt is t-Δ t Renewable energy output at all times; Nis the benchmark value of renewable energy power, which is generally taken as the rated installed capacity of renewable energy. Taking Δt as 1, that is, selecting the first-order difference of the renewable energy output power at adjacent moments to calculate the renewable energy volatility.

[0049] In order to improve the utilization rate of renewable energy and reduce the overall renewable energy volatility of the renewable energy station in the sending system, the best access location for energy storage is determined as follows:

[0050] (1) Establish a renewable energy output volatility index and calculate the renewable energy output volatility of each node before installing energy storage in the sending-end system; configure energy storage of the same capacity at each renewable energy node and then calculate the renewable energy output volatility respectively.

[0051] (2) Subtract the volatility of renewable energy output of each node after adding energy storage from the volatility of renewable energy output of the corresponding node before adding energy storage, sort the differences, observe the nodes where the volatility of renewable energy is most significantly reduced, and select N nodes to configure energy storage.

[0052] The energy storage capacity of each node of the selected energy storage configuration is taken as a variable, the minimum overall energy storage configuration capacity is taken as the objective function, and a variety of constraints are considered to establish an electrochemical energy storage optimization configuration model to optimize the energy storage capacity of the sending-end system.

[0053] Among them, the objective function and constraints of the model are as follows:

[0054] The objective function is to minimize the overall energy storage configuration capacity, which is expressed as the sum of all energy storage installed capacities of the selected nodes E total .

[0055]

[0056] In the formula, E n is the installed capacity of the nth energy storage, and N is the total number of nodes selected to be incorporated into the energy storage.

[0057] Constraints are divided into basic constraints and advanced constraints.

[0058] The basic constraints are defined as system constraints and energy storage constraints, where the system constraints are as follows:

[0059] (1) Power balance constraints

[0060] P dis (t)-P ch (t)+P G (t)+u PV (t)+u WT (t) = u L (t)(3)

[0061] Where P dis (t), P ch (t) are the energy storage discharge power and charging power at time t, respectively, P G (t) is the power generation capacity of the thermal power unit at time t, u PV (t),u WT (t) is the photovoltaic power generation and wind power generation at time t, u L (t) is the load demand power at time t.

[0062] (2) Constraints on renewable energy generation capacity

[0063] u min ≤u(t)≤u max (4)

[0064] Where u(t) is the power of each type of renewable energy source in period t; u min 、u max They are the maximum and minimum output power of each type of power supply respectively.

[0065] (3) Node voltage constraints

[0066]

[0067] In the formula, u i is the actual voltage of the ith node, u i is the node voltage lower limit of the ith node, is the node voltage upper limit of the i-th node.

[0068] The energy storage constraints are as follows:

[0069] (1) Energy storage charging and discharging power constraints

[0070]

[0071] Where P n,dis (t), P n,ch (t) are the discharge and charging power of the nth energy storage system at time t. n,bat.max is the upper limit of energy storage charging and discharging power, and μ is the fixed proportional coefficient between the upper limit of energy storage power and capacity. n,bat (t) is the energy storage charge and discharge identifier, charging is 1 and discharging is 0.

[0072] (2) Energy storage state of charge constraints

[0073]

[0074] Where Δt is the time step, which is 1h in this paper, and E n (0) is the initial capacity of the nth energy storage, SOCn,max , SOC n,min are the upper and lower limits of the state of charge of the nth energy storage, and η is the energy storage charging and discharging efficiency.

[0075] (3) Charge and discharge capacity constraints

[0076]

[0077] The advanced constraints are as follows:

[0078] The advanced constraint is the renewable energy utilization rate constraint, and its mathematical expression is:

[0079] η≥η1 (9)

[0080] Where η is the utilization rate of renewable energy, and η1 is the specified value of the utilization rate of renewable energy.

[0081] A particle swarm algorithm based on inertia weight is proposed to solve the electrochemical energy storage optimization configuration model and obtain the optimal energy storage configuration capacity. The particle swarm algorithm based on inertia weight is an improved particle swarm algorithm. By introducing new control particles, the inertia weight is controlled, the convergence speed is accelerated, and it avoids falling into the local optimum.

[0082] The particle swarm algorithm based on inertia weight is used to solve the electrochemical energy storage optimization configuration model, which includes the following steps:

[0083] Step 1: Initialize the velocity, position and inertia weight of each particle.

[0084] Step 2: Calculate the fitness of each particle, the individual optimal value of each particle, and the global optimal value of the particles as a whole.

[0085] Step 3: Update the particle's velocity, position, and inertia weight.

[0086] Step 4: Determine whether the model satisfies the constraints. If not, return to step 2; if satisfied, output the optimal energy storage configuration capacity.

[0087] Among them, the particle swarm algorithm of inertia weight is specifically expressed as follows:

[0088] The basic particle swarm algorithm is a swarm intelligence algorithm designed by simulating the predation behavior of bird flocks. The PSO algorithm compares the search space of the problem to the flight space of birds, and abstracts each bird as a particle without mass and volume to represent a possible solution to the problem. It regards the process of finding the optimal solution to the problem as the process of birds looking for food, thus being able to solve complex optimization problems. The particle swarm algorithm is a group of particles moving in the search space, subject to their own best past position p best and the best past position g of the entire group or neighborsbest The PSO is initialized as a group of random particles (random solutions). Then the optimal solution is found through iteration. In each iteration, the particles track two "extreme values" (p best , g best ) to update itself. After finding these two optimal values, the particle updates its speed and position using the following formula.

[0089] The i-th particle is represented as a D-dimensional vector:

[0090] X i =(x i1 ,x i2 …,x iD ),i=1,2,…,N(10)

[0091] The "flying" speed of the ith particle is also a vector of D, denoted as

[0092] V i =(v i1 ,v i2 …,v iD ),i=1,2,…,N(11)

[0093] The optimal position searched by the i-th particle so far is called the individual extreme value, denoted by

[0094] P best =(p i1 ,p i2 …,p iD ),i=1,2,…,N(12)

[0095] The optimal position searched by the entire particle swarm so far is the global extreme value, denoted as

[0096] g best =(g i1 ,g i2 …,g iD ),i=1,2,…,N(13)

[0097] When these two optimal values ​​are found, the particle updates its speed and position according to the following two equations:

[0098] v ij (t+1)=v ij (t)+c1r1(t)[p ij (t)-x ij (t)]+c2r2(t)[g ij (t)-x ij (t)](14)

[0099] x ij (t+1)=xij (t)+v ij (t+1)(15)

[0100] Among them: c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are uniform random numbers in the range of [0, 1], which increase the randomness of particle flight. ij is the velocity of the particle, v ij ∈[-v max ,v max ],v max is a constant set by the user to limit the speed of the particle.

[0101] The right side of formula (14) consists of three parts: the first part is the "inertia" part, which means that the particle has a tendency to maintain its previous speed; the second part is the "cognition" part, which reflects the particle's memory or recollection of its own historical experience, which means that the particle has a tendency to approach its own historical best position; the third part is the "social" part, which reflects the group historical experience of collaborative cooperation and knowledge sharing among particles, which means that the particle has a tendency to approach the historical best position of the group or neighborhood.

[0102] The improved particle swarm algorithm introduces new control particles to control the inertia weight to speed up the convergence and avoid falling into the local optimum. Its evolution process is:

[0103] v ij (t+1)=ω·v ij (t)+c1r1(t)[p ij (t)-x ij (t)]+c2r2(t)[g ij (t)-x ij (t)] (16)

[0104] x ij (t+1)=x ij (t)+v ij (t+1)(17)

[0105]

[0106] Among them, T max represents the maximum evolutionary generation; ω max represents the maximum inertia weight; ω min represents the minimum inertia weight; t represents the current iteration number. (Generally, ω max =0.9,ω min =0.4)

[0107] In formula (16), the first part represents the previous velocity of the particle, which is used to ensure the global convergence performance of the algorithm; the second and third parts enable the algorithm to have local convergence ability.

[0108] It can be seen that the inertia weight ω in formula (16) indicates the extent to which the original speed is retained. If ω is large, the global convergence ability is strong and the local convergence ability is weak. If ω is small, the local convergence ability is strong and the global convergence ability is weak.

[0109] When ω = 1, equation (16) is exactly the same as equation (14), indicating that the particle swarm algorithm with inertia weight is an extension of the basic particle swarm algorithm.

[0110] The experimental results show that when ω is between 0.8 and 1.2, the particle swarm algorithm has a faster convergence speed, while when ω>1.2, the algorithm is prone to fall into local extreme values. The calculation process of the improved particle swarm algorithm is as follows Figure 2 shown.

[0111] This disclosure also uses historical output data of a certain region as an example for simulation verification. Figure 3 As shown, it is a structural diagram of a regional power system, which is modeled and solved using the method disclosed in the present invention, wherein Table 1 is the output fluctuation rate of renewable energy of a regional renewable energy power bus. As can be seen from Table 1, the reduction in the volatility of renewable energy of bus No. 33 is the largest, indicating that installing energy storage on bus No. 33 can reduce the volatility of renewable energy to the greatest extent and improve the output stability of renewable energy.

[0112] Table 1 Renewable energy power bus renewable energy output fluctuation rate

[0113]

[0114] Table 2 shows the energy storage configuration results for a certain region.

[0115] Table 2 shows the energy storage configuration results

[0116]

[0117] Table 3 shows the energy storage configuration results under different renewable energy utilization rates in a certain region.

[0118] Table 3 shows the energy storage configuration results under different renewable energy utilization rates.

[0119]

[0120]

[0121] Finally, a curve of the relationship between the utilization rate of renewable energy and the energy storage capacity in a certain area is drawn, such as Figure 4 shown.

[0122] Example 2

[0123] This embodiment provides an electrochemical energy storage optimization configuration system taking into account the utilization rate of renewable energy, including:

[0124] The energy storage site selection module is configured to: establish a renewable energy output volatility index and select a site for electrochemical energy storage based on the index;

[0125] The model building module is configured to: take the energy storage capacity incorporated into each grid node of the selected energy storage configuration as a variable, establish an electrochemical energy storage optimization configuration model, and set the objective function and constraint conditions of the model;

[0126] The model solving module is configured to solve the electrochemical energy storage optimization configuration model using a particle swarm algorithm based on inertia weight to obtain the optimal energy storage configuration capacity.

[0127] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0128] In further embodiments, there is also provided:

[0129] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0130] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0131] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0132] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0133] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0134] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0135] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0136] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0137] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, and the like.

[0138] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0139] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. An electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy, characterized in that: The following steps are involved: Establishing renewable energy output volatility indicators and selecting electrochemical energy storage sites based on the indicators; The energy storage capacity of each grid node selected for energy storage configuration is used as a variable to establish an electrochemical energy storage optimization configuration model, and the objective function and constraint conditions of the model are set; The inertia weight-based particle swarm algorithm is used to solve the electrochemical energy storage optimization configuration model and obtain the optimal energy storage configuration capacity.

2. The electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy as claimed in claim 1, characterized in that: The renewable energy output volatility index is the ratio of the first-order difference of two output powers separated by a certain time in the wind power output time series to the power reference value.

3. The electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy as claimed in claim 1, characterized in that: The site selection for electrochemical energy storage is based on the above indicators, specifically: Calculate the output fluctuation rate of renewable energy at each node before adding energy storage to the sending-end system; Configure energy storage of the same capacity at each renewable energy node and calculate the output fluctuation rate of renewable energy respectively; The renewable energy output volatility of each node after adding energy storage is subtracted from the renewable energy output volatility of the corresponding node before adding energy storage, the differences are sorted, and N nodes with the most obvious reduction in renewable energy output volatility are selected to configure energy storage.

4. The electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy as claimed in claim 1, characterized in that: Taking the minimum overall configuration capacity of energy storage as the objective function, the constraints include basic constraints and advanced constraints, and the basic constraints include system constraints and energy storage constraints; Among them, the system constraints include power balance constraints, renewable energy power constraints and node voltage constraints, and the energy storage constraints include energy storage charging and discharging power constraints, energy storage charge state constraints and charging and discharging amount constraints.

5. The electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy as claimed in claim 4, characterized in that: The advanced constraint condition is the renewable energy utilization rate constraint, and its mathematical expression is: η≥η1 Where η is the utilization rate of renewable energy, and η1 is the specified value of the utilization rate of renewable energy.

6. The electrochemical energy storage optimization configuration method taking into account the utilization rate of renewable energy as claimed in claim 1, characterized in that: The inertia weight-based particle swarm algorithm is an improved particle swarm algorithm, which controls the inertia weight by introducing new control particles, accelerates the convergence speed, and avoids falling into the local optimum.

7. An electrochemical energy storage optimization configuration system taking into account the utilization rate of renewable energy, characterized in that: include: The energy storage site selection module is configured to: establish a renewable energy output volatility index and select a site for electrochemical energy storage based on the index; The model building module is configured to: take the energy storage capacity incorporated into each grid node of the selected energy storage configuration as a variable, establish an electrochemical energy storage optimization configuration model, and set the objective function and constraint conditions of the model; The model solving module is configured to solve the electrochemical energy storage optimization configuration model using a particle swarm algorithm based on inertia weight to obtain the optimal energy storage configuration capacity.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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