Energy storage system capacity configuration method based on artificial potential field algorithm

Abstract

The invention belongs to the technical field of energy storage, and particularly relates to an energy storage system capacity configuration method based on an artificial potential field algorithm, and the method comprises the steps: obtaining the capacity configuration preselection schemes of an electric energy storage device and a hydrogen energy storage device; operating the artificial potential field algorithm by using the pre-selected scheme, checking whether a power distribution result correspondingly output by the artificial potential field algorithm meets a capacity constraint of the pre-selected scheme or not to confirm whether the pre-selected scheme meets an operating condition of the artificial potential field algorithm or not, and taking the pre-selected scheme meeting the operating condition of the artificial potential field algorithm as an optimal scheme of capacity configuration; wherein when the number of the pre-selected schemes meeting the operation conditions of the artificial potential field algorithm is multiple, one scheme with the lowest purchase cost is selected from the multiple pre-selected schemes meeting the operation conditions of the artificial potential field algorithm to serve as the optimal scheme for capacity configuration, and the most economical energy storage system is constructed according to the optimal scheme. According to the method, the most economical capacity configuration scheme of the energy storage system is obtained through combination of off-line operation and on-line operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage, and in particular relates to a capacity configuration method of an energy storage system based on an artificial potential field algorithm. Background Art

[0002] With the increasing development of renewable energy, energy storage has become an important component of the power system. Moreover, as one of the most promising solutions to adapt to the intermittent and uncertain nature of renewable energy, energy storage is widely used to improve the stability and economy of the system. Electric energy storage is suitable for short-term (i.e., a few hours to a few days) and small and medium-sized energy storage applications. Among them, supercapacitors and batteries are usually used as short-term electric energy storage devices to store energy. Supercapacitors mainly include double-layer capacitors, lithium-ion capacitors, sodium-ion capacitors and other types. Batteries mainly include lithium-ion batteries, sodium-ion batteries, lithium metal batteries, semi-solid batteries, solid-state batteries and other types. Electric energy storage devices such as batteries and supercapacitors have significant advantages in power density. For long-term (i.e., weeks, months, seasons) and large-scale applications, hydrogen energy storage technology may be a better choice. Hydrogen energy storage technology usually includes devices such as electrolyzers, fuel cells, and hydrogen storage tanks to convert electrical energy into hydrogen energy storage, and convert hydrogen energy into electrical energy utilization. Hydrogen energy storage has significant advantages in energy density. Considering the differences between electric and hydrogen energy storage technologies in physical properties such as energy density, power density, cycle life and energy efficiency, hybrid energy storage systems combining the two technologies are under study.

[0003] In order to improve the economy of energy storage systems, appropriate capacity configuration is required for energy storage components. The capacity configuration and energy management of energy storage systems are actually two tightly coupled issues that complement each other and can rely on each other to achieve the global optimum of system economy. However, energy management strategies based on filtering methods are usually not combined with component capacity optimization because the former is real-time and the latter is offline. Therefore, the offline capacity configuration scheme has certain limitations. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to propose a capacity configuration method for an energy storage system, which integrates an optimized energy management strategy for the filtering method (optimizing the energy management strategy based on the filtering method using an artificial potential field algorithm), and obtains the most economical capacity configuration plan for the energy storage system through a combination of offline and online calculations.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a method for configuring the capacity of an energy storage system based on an artificial potential field algorithm, comprising: obtaining a preselected scheme for capacity configuration of an electric energy storage device and a hydrogen energy storage device; using the preselected scheme to run the artificial potential field algorithm, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and taking the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm, one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on the preselected scheme.

[0006] According to a specific embodiment of the present invention, the step of obtaining a pre-selected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device includes: using a particle swarm optimization algorithm to calculate the most ideal pre-selected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0007] According to a specific embodiment of the present invention, the step of using a particle swarm optimization algorithm to calculate the most ideal pre-selected solution for capacity configuration of an electric energy storage device and a hydrogen energy storage device includes: obtaining the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device in the built energy storage system as the initial value of the particle swarm optimization algorithm; using the procurement cost of the energy storage system as the fitness value of the particle swarm optimization algorithm, and iteratively calculating the particle swarm optimization algorithm to obtain the most ideal pre-selected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0008] According to a specific embodiment of the present invention, the steps of running the artificial potential field algorithm using the preselected scheme, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and using the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration include: for a preselected scheme, running the artificial potential field algorithm according to the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device, and calculating the power allocation results of the electric energy storage device and the hydrogen energy storage device; wherein the power allocation results include the input / output power range interval of the electric energy storage device within a preset time period, and the input / output power range interval of the hydrogen energy storage device within the preset time period; judging the input power of the electric energy storage device The method further comprises the following steps: determining whether the input / output power range of the electric energy storage device and the input / output power range of the hydrogen energy storage device exceed the capacity configuration parameters of the hydrogen energy storage device in the preselected scheme: if both the input / output power range of the electric energy storage device and the input / output power range of the hydrogen energy storage device do not exceed the corresponding capacity configuration parameters, then the power allocation result is deemed to meet the capacity constraints of the preselected scheme, and the preselected scheme meets the operating conditions of the artificial potential field algorithm; if the input / output power range of the electric energy storage device and / or the input / output power range of the hydrogen energy storage device exceed the corresponding capacity configuration parameters, then the power allocation result is deemed to not meet the capacity constraints of the preselected scheme, and the preselected scheme does not meet the operating conditions of the artificial potential field algorithm.

[0009] According to a specific embodiment of the present invention, the step of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device based on the artificial potential field algorithm includes: pre-defining a corresponding virtual force based on the state of charge of the electric energy storage device; determining a corresponding power distribution factor according to the virtual force of the electric energy storage device regarding its state of charge, and using the power distribution factor to adjust the cutoff frequency of the low-pass filter in the energy storage system to adjust the input / output power of the electric energy storage device; and synchronously adjusting the input / output power of the hydrogen energy storage device according to the input / output power of the electric energy storage device.

[0010] According to a specific embodiment of the present invention, the virtual force F sc as follows:

[0011]

[0012] Among them, a is the shaping parameter preset by the virtual force, SOC min Indicates the preset minimum value of the state of charge of the energy storage device, SOC max Indicates the preset maximum value of the state of charge of the energy storage device, SOC mid Indicates the preset intermediate value of the state of charge of the energy storage device, i.e. SOC min<SOC mid <SOC max .

[0013] According to a specific embodiment of the present invention, the power allocation factor K sc as follows:

[0014]

[0015] Among them, K scmid represents the preset initial parameters of the power allocation factor, F sc Indicates the virtual force corresponding to the charge state of the electric energy storage device.

[0016] According to a specific embodiment of the present invention, the optimized artificial potential field algorithm is run using the preselected scheme, and it is verified whether the power allocation result corresponding to the output of the optimized artificial potential field algorithm meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the optimized artificial potential field algorithm, and the preselected scheme that meets the operating conditions of the optimized artificial potential field algorithm is used as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on it.

[0017] According to a specific embodiment of the present invention, the artificial potential field algorithm is optimized according to the following steps: a particle swarm optimization algorithm is used to calculate the optimized parameter values ​​of the shaping parameters preset by the virtual force of the charge state of the electric energy storage device in the artificial potential field algorithm, and the optimized parameter values ​​of the initial parameters of the power allocation factor corresponding to the electric energy storage device, so as to run the artificial potential field algorithm according to the optimized parameter values.

[0018] According to a specific embodiment of the present invention, the particle swarm optimization algorithm uses the original parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor as initial values, and the loss cost of the energy storage system as the fitness value, and performs iterative calculation to obtain the optimized parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor.

[0019] The present invention provides a capacity configuration method for an energy storage system based on an artificial potential field algorithm, which obtains the most economical capacity configuration scheme for the energy storage system by combining off-line calculation and on-line calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention provides a flow chart of a specific embodiment of a method for configuring energy storage system capacity based on an artificial potential field algorithm. DETAILED DESCRIPTION

[0021] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0023] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, structures and devices known to the public are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0024] See also Figure 1 A method for configuring energy storage system capacity based on an artificial potential field algorithm is shown, comprising:

[0025] Step S100, obtaining a pre-selected solution regarding capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0026] Step S200, using the pre-selected scheme to run the artificial potential field algorithm, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the pre-selected scheme, so as to confirm whether the pre-selected scheme meets the operating conditions of the artificial potential field algorithm, and taking the pre-selected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration;

[0027] Among them, when there are multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm as the optimal solution for capacity configuration, so as to build the most economical energy storage system based on it.

[0028] First, for energy storage systems that have not yet been built, whether considering the economic benefits of hydrogen production or the corresponding construction costs, the capacity of the hydrogen energy storage device and the capacity of the electric energy storage device need to be reasonably configured. In this regard, in a specific embodiment, the following objective function is constructed for the capacity configuration scheme of the hydrogen energy storage device and the electric energy storage device:

[0029] J cost =J elsize +J scsize , and J elsize =x el P elbmax C el , J scsize =E sc C sc ,

[0030] Among them, J cost represents the objective function, and the objective function corresponds to the procurement cost of hydrogen energy storage device and electric energy storage device, J elsize represents the purchase cost of the electrolyzer, J scsize represents the purchase cost of supercapacitors, x el Indicates the number of cells in the electrolytic cell, P elbmax Indicates the maximum input power of the electrolyzer, that is, x el P elbmax Indicates the capacity of the electrolytic cell, C el Represents the purchase cost per unit capacity of the electrolytic cell, E sc Indicates the capacity of the supercapacitor, C sc Indicates the purchase cost per unit capacity of supercapacitor.

[0031] Furthermore, the purchase cost per unit capacity of the electrolyzer can be expressed as:

[0032]

[0033] C elpc represents the unit purchase price of the electrolytic cell, y el represents the service life of the electrolytic cell, r represents the corresponding annual interest rate, T year Indicates the number of days in a year, T test Indicates the number of days in a year that the electrolyzer is actually tested.

[0034] The purchase cost of supercapacitor per unit capacity can be expressed as:

[0035]

[0036] C scpc Indicates the unit purchase price of supercapacitor, y sc represents the service life of the supercapacitor, r represents the corresponding annual interest rate, T test Indicates the number of days in a year when the supercapacitor is actually tested.

[0037] It should be noted that in this embodiment, the hydrogen energy storage device is specifically taken as an electrolyzer, but it is not used to limit the components included in the hydrogen energy storage device. For example, it can also include a fuel cell, a hydrogen storage tank, etc. Similarly, the electric energy storage device is specifically taken as a supercapacitor. In practical applications, the electric energy storage device can also use a battery, or a mixture of a supercapacitor and a battery. There are no excessive restrictions on this. Without departing from the spirit of the present invention, the modifications and embellishments made to the embodiments of the present invention by those skilled in the art still fall within the scope of the invention patent application of the present invention.

[0038] Based on the above, by considering the procurement cost of the energy storage system, that is, using the objective function, multiple possible pre-selected solutions for the capacity configuration of the electric energy storage device and the hydrogen energy storage device can be calculated accordingly. In this embodiment, in order to directly obtain the most ideal pre-selected solution, the particle swarm optimization algorithm is used to calculate the optimal capacity configuration of the electric energy storage device and the hydrogen energy storage device. The specific key steps can be referred to as follows:

[0039] Step 1: Initialize a set of particles with random positions and velocities, where the position of a specific particle represents the parameters related to the capacity of the electric energy storage device and the hydrogen energy storage device that need to be obtained, that is, x in the above formula el and E sc .

[0040] It should be noted that during the particle swarm optimization algorithm operation, the corresponding initial value needs to be added to the obtained variable parameter, so as to obtain the target value of the variable parameter through multiple iterations. el and E sc It is necessary to use the capacity configuration parameters of the electrolyzer and supercapacitor in the built energy storage system as the initial value, that is, to use the x el and E sc The actual parameter value is used as x in the particle swarm optimization algorithm el and E sc The initial value of .

[0041] Step 2: Since x el and E sc are respectively related to the capacity of the electric energy storage device and the hydrogen energy storage device. According to the above x el and E sc The initial value of can determine the power distribution between the electrolyzer and the supercapacitor in actual applications. When the energy management strategy is used to adjust the power distribution between the electrolyzer and the supercapacitor, the input power of the electrolyzer, the input / output power of the supercapacitor, and the state of charge (SOC) of the supercapacitor can be output.

[0042] Step 3: Calculate the fitness value of the particle according to the output of the energy management strategy. Specifically, the procurement cost of the energy storage system, i.e., the J cost As the fitness value. For each particle, calculate the corresponding fitness value.

[0043] Step 4: Update the best fitness value of each particle, i.e., the minimum J, by comparing the current fitness value of each particle with the previous best fitness value. cost For a particular particle, the position associated with its best fitness value is denoted by X p .

[0044] Step 5: Update the best fitness value of the group by comparing the current fitness values ​​of all particles with the previous best fitness value of the group. For the group, the position associated with the best fitness value of the group is recorded as X g .

[0045] Step 6: For each particle, update its velocity and position as follows:

[0046] V(i+1)=wV(i)+c1r1(X p (i)-X(i))+c2r2(X g (i)-X(i)),

[0047] X(i+1)=X(i)+V(i+1),

[0048] Where V and X are the velocity and position of a specific particle, i is the current iteration index, w is the inertia weight, c1 and c2 are two acceleration constants, and r1 and r2 are two random numbers in [0,1]. If the iteration termination condition is not met, loop to step 2 for the next iteration, otherwise, terminate the particle swarm optimization algorithm and return x el and E sc target value, that is, the optimal capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device.

[0049] It can be seen that the above-mentioned pre-selected solutions for obtaining the capacity configuration of the electric energy storage device and the hydrogen energy storage device are all obtained by offline operation calculation. In order to confirm whether they can be applied in actual applications, the energy management strategy of the online operation of the built energy storage system can be used to verify them to confirm their feasibility. In this embodiment, the energy management strategy based on the artificial potential field algorithm is used to verify the above-mentioned most ideal pre-selected solutions for the capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0050] It should be noted here that the energy management strategy based on the artificial potential field algorithm is to use the artificial potential field algorithm to adjust the cutoff frequency of the low-pass filter, and the low-pass filter can allocate the high-frequency part of the power to the electric energy storage device and the low-frequency part to the hydrogen energy storage device, or use the low-pass filter to make the electric energy storage device provide high-frequency power and the hydrogen energy storage device provide low-frequency power. Since the above process of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device through the low-pass filter requires adjusting the input / output power of the electric energy storage device, in order to avoid overcharging or over-discharging, it is necessary to maintain the SOC of the electric energy storage device stable. Therefore, the artificial potential field algorithm is used to optimize the power distribution of the electric energy storage device.

[0051] In a specific embodiment, a supercapacitor is used as an example for an electric energy storage device. First, the virtual force of the supercapacitor is determined using an artificial potential field, and the virtual force F sc It can be expressed as:

[0052]

[0053] Where x = SOC-SOC mid , while SOC mid is a value between the preset minimum SOC min and the preset maximum SOC max The value between is the SOC that the supercapacitor expects to operate, which can be adjusted accordingly according to the actual situation. a is the shaping parameter preset by the virtual force, which determines the virtual force F sc The rate of change.

[0054] Secondly, the virtual force F can be used sc To calculate the power allocation factor K corresponding to the supercapacitor sc , and the calculation formula is as follows:

[0055]

[0056] Among them, K scmid It is the preset initial parameter used to adjust the input / output power of the supercapacitor.

[0057] Finally, the power allocation factor K can be calculated according to sc The input power of the supercapacitor during charging or the output power during discharging is adjusted, that is, the cutoff frequency of the low-pass filter is correspondingly adjusted, so that the net load power of the system can be reasonably distributed and the overcharging or over-discharging of the supercapacitor can be fully avoided.

[0058] Furthermore, considering the loss cost of the energy storage system, the energy management strategy based on the artificial potential field algorithm can be further optimized. Accordingly, the optimized energy management strategy can be used to verify the most ideal pre-selected plan for capacity configuration of the above-mentioned electric energy storage device and hydrogen energy storage device.

[0059] Specifically, the optimization of the energy management strategy based on the artificial potential field algorithm is achieved by optimizing the empirical parameters in the artificial potential field algorithm, that is, for the shaping parameter a mentioned in the artificial potential field algorithm, and the initial parameter K of the power allocation factor scmid In this regard, the optimization goal is to reduce the loss cost of the energy storage system as much as possible. The particle swarm optimization algorithm can be used to directly calculate a and K scmid The optimal parameter value is used as the optimized parameter value.

[0060] It should be noted that the loss cost of the energy storage system can be expressed as J eldeg +J scele , where J eldeg represents the degradation cost of the electrolyzer, and J scele Indicates the deviation cost of the supercapacitor's SOC from the preset value.

[0061] Correspondingly, J eldeg It can be expressed as:

[0062] Among them, U elbeol Indicates the voltage drop of the electrolytic cell before reaching the end of its service life, ΔU elb Indicates the real-time voltage drop of the electrolytic cell.

[0063] J scele It can be expressed as: J scele =β ele E sc |SOC end -SOC mid |,

[0064] Among them, β ele Indicates electricity price, SOC end represents the final value of the supercapacitor, while SOC mid The preset intermediate value of supercapacitor SOC.

[0065] Based on the above, the particle swarm optimization algorithm is used to calculate the initial parameters K of the shaping parameter a and the power allocation factor: scmid The steps to find the optimal parameter values ​​are as follows:

[0066] Step 1: Initialize a set of particles with random positions and velocities, where the position of a specific particle represents the a and K that need to be obtained scmid Optimized parameter values.

[0067] Similarly, using a and K in the artificial potential field algorithm scmid The original parameter values ​​are used as the initial values ​​of the particle swarm optimization algorithm.

[0068] Step 2: Based on a and K scmid The initial value of can determine the power distribution between the electrolyzer and the supercapacitor. When the energy management strategy is used to adjust the power distribution between the electrolyzer and the supercapacitor, the input power of the electrolyzer, the input / output power of the supercapacitor, and the supercapacitor SOC can be output.

[0069] Step 3: Calculate the fitness value of the particle according to the output of the energy management strategy. Specifically, use the loss cost of the energy storage system as the fitness value. For each particle, calculate the corresponding fitness value.

[0070] Step 4: By comparing the current fitness value of each particle with the previous best fitness value, update the best fitness value of each particle, that is, minimize the loss cost of the energy storage system. For a specific particle, the position associated with its best fitness value is recorded as X p .

[0071] Step 5: Update the best fitness value of the group by comparing the current fitness values ​​of all particles with the previous best fitness value of the group. For the group, the position associated with the best fitness value of the group is recorded as X g .

[0072] Step 6: For each particle, update its velocity and position as follows:

[0073] V(i+1)=wV(i)+c1r1(X p (i)-X(i))+c2r2(X g (i)-X(i)),

[0074] X(i+1)=X(i)+V(i+1),

[0075] If the iteration termination condition is not met, loop to step 2 for the next iteration, otherwise, terminate the particle swarm optimization algorithm and return a and K scmid The optimal parameter values ​​are used to optimize the artificial potential field algorithm.

[0076] The steps for verifying the pre-selected solution using the artificial potential field algorithm can be referred to as follows:

[0077] When the built energy storage system uses the artificial potential field algorithm to adjust the power distribution between the electric energy storage device and the hydrogen energy storage device, it can be operated according to the most ideal pre-selected plan obtained above, so as to calculate the power distribution results of the electric energy storage device and the hydrogen energy storage device, and verify whether the power distribution results meet the capacity constraints of the pre-selected plan to confirm whether the pre-selected plan meets the operating conditions of the artificial potential field algorithm.

[0078] When the power allocation result meets the capacity constraint of the pre-selected scheme, it means that the pre-selected scheme meets the operating conditions of the artificial potential field algorithm and can be used to build an energy storage system in practical applications; when the power allocation result does not meet the capacity constraint of the pre-selected scheme, it means that the pre-selected scheme does not meet the operating conditions of the artificial potential field algorithm and cannot support practical applications.

[0079] Specifically, the input / output power of the online allocated electric energy storage device and hydrogen energy storage device is used to determine whether the calculated corresponding capacity can meet the input / output power:

[0080] If the capacity configuration in the pre-selected scheme can satisfy the corresponding input / output power calculated using the artificial potential field algorithm, that is, the input / output power range of the electric energy storage device does not exceed the capacity configuration parameters in the pre-selected scheme, and the input / output power range of the hydrogen energy storage device does not exceed the capacity configuration parameters in the pre-selected scheme, it means that the power allocation result meets the capacity constraints of the pre-selected scheme.

[0081] If the capacity configuration in the preselected scheme cannot satisfy the corresponding input / output power calculated using the artificial potential field algorithm, that is, the input / output power range of the electric energy storage device exceeds the capacity configuration parameters in the preselected scheme, and / or the input / output power range of the hydrogen energy storage device exceeds the capacity configuration parameters in the preselected scheme, it means that the power allocation result does not meet the capacity constraints of the preselected scheme.

[0082] Correspondingly, the pre-selected schemes that do not meet the operating conditions of the artificial potential field algorithm are directly discarded, and since the particle swarm optimization algorithm is used in this embodiment to directly obtain the most ideal pre-selected scheme, if the scheme meets the operating conditions of the artificial potential field algorithm, then it is the optimal scheme for capacity configuration.

[0083] It should be noted here that the process of verifying the capacity configuration in the pre-selected scheme using the artificial potential field algorithm is to obtain the range interval of the input / output power fluctuation change calculated by the electric energy storage device and the hydrogen energy storage device within the preset time period, and use the capacity configuration parameters in the pre-selected scheme as the upper and lower limits of the power, that is, when the artificial potential field algorithm is enabled to calculate the power allocation results of the electric energy storage device and the hydrogen energy storage device, the input / output power that needs to be adjusted for the electric energy storage device at multiple different time nodes is recorded to form a continuous power range interval, and the input / output power that needs to be adjusted for the hydrogen energy storage device at multiple different time nodes is recorded to form another continuous power range interval, and it is respectively identified whether the power range interval of the electric energy storage device exceeds the capacity configuration parameters of the electric energy storage device in the pre-selected scheme, and whether the power range interval of the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the pre-selected scheme. If at least one of the electric energy storage device and the hydrogen energy storage device exceeds, it means that the pre-selected scheme does not meet the operating conditions of the artificial potential field algorithm.

[0084] It should also be added that, since the particle swarm optimization algorithm is used in this embodiment to directly obtain the most ideal pre-selected solution, of course, other intelligent algorithms, such as the whale optimization algorithm, etc., can also be used to obtain the most ideal pre-selected solution. Accordingly, it is only necessary to verify whether it meets the operating conditions of the artificial potential field algorithm. In addition, when obtaining the pre-selected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device, the enumeration method can also be used to list all possible capacity configuration solutions one by one, that is, there are multiple pre-selected solutions. Correspondingly, all the pre-selected solutions are verified one by one according to the above steps, and one of the pre-selected solutions that meet the operating conditions of the artificial potential field algorithm is selected as the optimal solution for capacity configuration, that is, the pre-selected solution with the lowest procurement cost is selected as the optimal solution for capacity configuration.

[0085] Similarly, when optimizing the artificial potential field algorithm, the initial parameters K of the shaping parameter a and the power allocation factor are calculated. scmid The optimized parameter values ​​are not limited to the particle swarm optimization algorithm, but can also be other intelligent algorithms.

[0086] Based on the optimal capacity configuration scheme obtained above, the most economical energy storage system can be constructed accordingly.

[0087] It should be noted that the step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0088] In summary, the present invention provides a method for configuring the capacity of an energy storage system based on an artificial potential field algorithm, which obtains the most economical capacity configuration scheme of the energy storage system by combining offline calculations with online calculations.

[0089] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for configuring energy storage system capacity based on artificial potential field algorithm, characterized in that: include: Obtaining a pre-selected solution regarding capacity configuration of the electric energy storage device and the hydrogen energy storage device; Using the pre-selected scheme to run the artificial potential field algorithm, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the pre-selected scheme, so as to confirm whether the pre-selected scheme meets the operating conditions of the artificial potential field algorithm, and taking the pre-selected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration; Among them, when there are multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm as the optimal solution for capacity configuration, so as to build the most economical energy storage system based on it.

2. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 1 is characterized in that: The step of obtaining a pre-selected solution regarding capacity configuration of the electric energy storage device and the hydrogen energy storage device includes: The particle swarm optimization algorithm is used to calculate the most ideal pre-selected plan for capacity configuration of electric energy storage devices and hydrogen energy storage devices.

3. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 2 is characterized in that: The steps of using the particle swarm optimization algorithm to calculate the most ideal pre-selected solution for the capacity configuration of the electric energy storage device and the hydrogen energy storage device include: Obtain the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device in the built energy storage system as the initial values ​​of the particle swarm optimization algorithm; The procurement cost of the energy storage system is used as the fitness value of the particle swarm optimization algorithm, and the optimal pre-selected plan for the capacity configuration of the electric energy storage device and the hydrogen energy storage device is obtained through iterative calculation of the particle swarm optimization algorithm.

4. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 1 is characterized in that: The steps of running the artificial potential field algorithm using the preselected scheme, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and taking the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration include: For a preselected solution, an artificial potential field algorithm is run according to the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device, and power allocation results of the electric energy storage device and the hydrogen energy storage device are calculated; wherein the power allocation results include an input / output power range interval of the electric energy storage device within a preset time period, and an input / output power range interval of the hydrogen energy storage device within a preset time period; Determine whether the input / output power range of the electric energy storage device exceeds the capacity configuration parameters of the electric energy storage device in the preselected solution, and determine whether the input / output power range of the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the preselected solution: If the input / output power range interval of the electric energy storage device and the input / output power range interval of the hydrogen energy storage device do not exceed the corresponding capacity configuration parameters, it is deemed that the power allocation result meets the capacity constraint of the pre-selected scheme, and the pre-selected scheme meets the operating conditions of the artificial potential field algorithm; If the input / output power range interval of the electric energy storage device and / or the input / output power range interval of the hydrogen energy storage device exceeds the corresponding capacity configuration parameters, it is deemed that the power allocation result does not meet the capacity constraint of the pre-selected scheme, and the pre-selected scheme does not meet the operating conditions of the artificial potential field algorithm.

5. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 1 is characterized in that: The steps of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device based on the artificial potential field algorithm include: Predefine a corresponding virtual force based on the state of charge of the electric energy storage device; Determine a corresponding power allocation factor according to the virtual force of the electric energy storage device with respect to its state of charge, and use the power allocation factor to adjust the cutoff frequency of the low-pass filter in the energy storage system to adjust the input / output power of the electric energy storage device; The input / output power of the hydrogen energy storage device is synchronously adjusted according to the input / output power of the electric energy storage device.

6. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 5 is characterized in that: Virtual force F sc as follows: x=SOC-SOC mid , Among them, a is the shaping parameter preset by the virtual force, SOC min Indicates the preset minimum value of the state of charge of the energy storage device, SOC max Indicates the preset maximum value of the state of charge of the energy storage device, SOC mid Indicates the preset intermediate value of the state of charge of the energy storage device, i.e. SOC min <SOC mid <SOC max .

7. The method for configuring energy storage system capacity based on artificial potential field algorithm according to claim 5, characterized in that: The power allocation factor K sc as follows: Among them, K scmid represents the preset initial parameters of the power allocation factor, F sc Indicates the virtual force corresponding to the charge state of the electric energy storage device.

8. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 1 is characterized in that: The optimized artificial potential field algorithm is run using the pre-selected scheme, and the power allocation result corresponding to the output of the optimized artificial potential field algorithm is verified to see whether it meets the capacity constraint of the pre-selected scheme, so as to confirm whether the pre-selected scheme meets the operating conditions of the optimized artificial potential field algorithm, and the pre-selected scheme that meets the operating conditions of the optimized artificial potential field algorithm is used as the optimal scheme for capacity configuration; wherein, when there are multiple pre-selected schemes that meet the operating conditions of the optimized artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple pre-selected schemes that meet the operating conditions of the optimized artificial potential field algorithm as the optimal scheme for capacity configuration, so as to build the most economical energy storage system based on the pre-selected scheme.

9. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 8 is characterized in that: The artificial potential field algorithm is optimized in the following steps: The particle swarm optimization algorithm is used to calculate the optimized parameter values ​​of the shaping parameters preset by the virtual force of the charge state of the electric energy storage device in the artificial potential field algorithm, as well as the optimized parameter values ​​of the initial parameters of the power allocation factor corresponding to the electric energy storage device, so as to run the artificial potential field algorithm according to the optimized parameter values.

10. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 9, characterized in that: The particle swarm optimization algorithm uses the original parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor as initial values, and the loss cost of the energy storage system as the fitness value, and performs iterative calculation to obtain the optimized parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor.

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